R Notebook

setwd("C:/Users/Architect_shwet/Desktop/New folder/credit card fraud detection")
library(dplyr)
library(ggplot2)
library(readr)#faster data reading
library(ggridges)
library(highcharter)
library(purrr)
library(keras)
library(tidyr)
library(caret)

Loading required package: lattice

Attaching package: <U+393C><U+3E31>caret<U+393C><U+3E32>

The following object is masked from <U+393C><U+3E31>package:purrr<U+393C><U+3E32>:

    lift

fraud <- read.csv('creditcard.csv')
glimpse(fraud)
head(fraud,5)
dim(fraud)
sapply(fraud, class)
table(fraud$Class)

#modifying time variable in hours
time <- fraud %>% select(Time)
head(time,4)

time <- time %>% mutate( Time = (Time/(60*60)))

hchart(time$Time, color = "purple", name = "Time in hours") %>%
  hc_title( text = "Histogram of Time taken for each transaction in hours", align = "center") %>%
  hc_exporting(enabled = T) %>%
  hc_add_theme(hc_theme_elementary())

#histogram of amount till 90 percentile
ggplot(aes(x=Amount),data = fraud) +
  geom_histogram(color="black",fill="green",alpha=0.8,bins=30) +
  scale_x_continuous(breaks=seq(0,1000,100),limits=c(0,quantile(fraud$Amount,c(0.99)))) +
  scale_y_continuous(limits=c(0,30000)) +
  ggtitle("Histogram of Transaction Amount") +
  xlab("Amount till 90 percentile") +
  ylab("Frequency")

For an autoencoder to work well we have a strong initial assumption:

that the distribution of variables for normal transactions is different from the distribution for

fraudulent ones. Let’s make some plots to verify this.

Variables were transformed to a [0,1] interval for plotting.

attach(fraud)

The following objects are masked from fraud (pos = 5):

    Amount, Class, Time, V1, V10, V11, V12, V13, V14, V15, V16, V17, V18, V19, V2, V20, V21,
    V22, V23, V24, V25, V26, V27, V28, V3, V4, V5, V6, V7, V8, V9

fraud %>%
  gather(variable, value, -Class) %>%
  ggplot(aes(y = as.factor(variable), 
             fill = as.factor(Class), 
             x = percent_rank(value))) +
  geom_density_ridges() +
  labs(x="Normalized variable",y="Variable",fill="Distribution of Fraud and Non-fraud")

We can see that distributions of variables for fraudulent transactions are very different then from normal ones, except for the Time variable,

which seems to have the exact same distribution.

#let's check the distribution of Amount and The normal and fradulent transactions
ggplot(fraud,aes(y=Amount,x=Class)) + 
  geom_boxplot(aes(group=Class)) +
  scale_y_continuous(limits=c(0,quantile(Amount,c(0.99))))

ggplot(fraud,aes(x=Time,y=Amount,color=as.factor(Class))) + 
  geom_point() +
  #adding custom colors
  labs(x="Time",y="amount",title="Scatter plot of Time vs Amount colored by type of transaction",
       color="Fraud")

#Spliting training set into two parts based on outcome: 75% and 25%
index <- createDataPartition(fraud$Class, p=0.75, list=FALSE)
trainSet <- fraud[ index,]
testSet <- fraud[-index,]

dim(trainSet)

[1] 213606     31

#Normalizing the inputs
#min-max normalization
#function to extract descriptive statistic params to be used in a min-max normalization function
desc_stat<- function(x)
{
  map(x,~list(
    min = min(.x),
    max = max(.x),
    mean = mean(.x),
    sd = sd(.x)
  ))
}

# Given a dataset and normalization constants it will create a min-max normalized
# version of the dataset.
minmax_norm <- function(x, desc) {
  map2_dfc(x, desc, ~(.x - .y$min)/(.y$max - .y$min))
}

#getting descriptive stattistics parameters for train and test data
desc_train<-trainSet %>% 
  select(-Class) %>% 
    desc_stat()

head(desc_train, 3)

$Time
$Time$min
[1] 0

$Time$max
[1] 172792

$Time$mean
[1] 94813.26

$Time$sd
[1] 47484.14


$V1
$V1$min
[1] -56.40751

$V1$max
[1] 2.45493

$V1$mean
[1] -0.002450399

$V1$sd
[1] 1.964192


$V2
$V2$min
[1] -72.71573

$V2$max
[1] 22.05773

$V2$mean
[1] -0.002099517

$V2$sd
[1] 1.664487

desc_test <-testSet %>% 
  select(-Class) %>% 
  desc_stat()

#training inputs
x_train<- trainSet %>% select(-Class) %>% 
    minmax_norm(desc_train) %>% 
    as.matrix()

dim(x_train)

[1] 213606     30

x_test<- testSet %>% select(-Class) %>% 
    minmax_norm(desc_test) %>% 
    as.matrix()

head(x_train, 3)

             Time        V1        V2        V3        V4        V5        V6        V7        V8
[1,] 0.000000e+00 0.9785420 0.7700667 0.8402985 0.2717965 0.7661203 0.2621917 0.2648754 0.7862984
[2,] 5.787305e-06 0.9418780 0.7653040 0.8684836 0.2136612 0.7656469 0.2755592 0.2668031 0.7894342
[3,] 1.157461e-05 0.9386168 0.7765198 0.8642507 0.2697964 0.7629751 0.2639842 0.2689678 0.7824835
            V9       V10       V11       V12       V13       V14       V15       V16       V17
[1,] 0.4518299 0.5052673 0.3811877 0.7430826 0.4861902 0.6412190 0.3838397 0.4542721 0.7277940
[2,] 0.4126948 0.5075850 0.2718174 0.7094865 0.4876347 0.6363721 0.2891244 0.4049313 0.7112528
[3,] 0.4889442 0.5243028 0.2363546 0.7231205 0.5525089 0.6084059 0.3494188 0.4246279 0.7242425
           V18       V19       V20       V21       V22       V23       V24       V25       V26
[1,] 0.6406811 0.5527877 0.5795298 0.5578399 0.4802370 0.6713734 0.3364400 0.5872903 0.4460130
[2,] 0.7884915 0.4677830 0.5780502 0.5597337 0.5102770 0.6670139 0.2238259 0.6142454 0.3891967
[3,] 0.6506652 0.6270327 0.5846153 0.5613275 0.5472707 0.6678042 0.4012698 0.5663427 0.5074968
           V27       V28       Amount
[1,] 0.4163451 0.3134227 0.0001047053
[2,] 0.4176687 0.3143710 0.0048071010
[3,] 0.4205610 0.3174900 0.0027242834

#class lebels for training data
y_train<-trainSet %>% select(Class) 
#Class labels for test data
y_test<-testSet %>% select(Class)  

#generating a symmetric autoencodes with 3 dense layers
model <- keras_model_sequential()

model %>%
  layer_dense(units = 15, activation = "tanh", input_shape = ncol(x_train)) %>%
  layer_dense(units = 8, activation = "tanh") %>%
  layer_dense(units = ncol(x_train))

summary(model)

________________________________________________________________________________________________________
Layer (type)                                  Output Shape                              Param #         
========================================================================================================
dense_1 (Dense)                               (None, 15)                                465             
________________________________________________________________________________________________________
dense_2 (Dense)                               (None, 8)                                 128             
________________________________________________________________________________________________________
dense_3 (Dense)                               (None, 30)                                270             
========================================================================================================
Total params: 863
Trainable params: 863
Non-trainable params: 0
________________________________________________________________________________________________________

let’s compile and define the loss function and optimization streategy to use-

we will use ADAM as the optimizer to optimze the network and update weights and params

and minimize the loss

model %>%
  compile(loss = "mean_squared_error", optimizer = "adam")

let’s train the model

we will only feed our model the normal(non-fradulent) cases, so that it is able to encode

that how and differentiate between normal and non-fradulent cases using the different distribution

amongst both

model %>%
  fit(x = x_train[y_train == 0,],
      y = x_train[y_train == 0,],
      epochs = 100,
      batch_size = 32,
      validation_data = list(x_test[y_test == 0,], x_test[y_test == 0,]),
      callbacks = callback_tensorboard(log_dir = "logs/run_a")
      )

2018-04-16 18:20:04.336805: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2018-04-16 18:20:04.366879: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2018-04-16 18:20:04.369050: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2018-04-16 18:20:04.371245: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2018-04-16 18:20:04.373417: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2018-04-16 18:20:04.375956: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-04-16 18:20:04.378785: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-04-16 18:20:04.381607: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.

Train on 213235 samples, validate on 71080 samples
Epoch 1/100

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213235/213235 [==============================] - 13s - loss: 0.0054 - val_loss: 0.0115
Epoch 2/100

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213235/213235 [==============================] - 13s - loss: 6.9094e-04 - val_loss: 0.0103
Epoch 3/100

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Epoch 4/100

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Epoch 5/100

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Epoch 6/100

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213235/213235 [==============================] - 25s - loss: 4.4130e-04 - val_loss: 0.0102
Epoch 8/100

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Epoch 9/100

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Epoch 10/100

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Epoch 11/100

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213235/213235 [==============================] - 15s - loss: 4.3869e-04 - val_loss: 0.0101
Epoch 12/100

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Epoch 13/100

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Epoch 14/100

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Epoch 15/100

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Epoch 16/100

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Epoch 18/100

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Epoch 19/100

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Epoch 20/100

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Epoch 21/100

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Epoch 22/100

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Epoch 23/100

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Epoch 24/100

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213235/213235 [==============================] - 15s - loss: 4.3712e-04 - val_loss: 0.0099
Epoch 25/100

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Epoch 26/100

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Epoch 27/100

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Epoch 28/100

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213235/213235 [==============================] - 10s - loss: 4.3690e-04 - val_loss: 0.0098
Epoch 29/100

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213235/213235 [==============================] - 10s - loss: 4.3691e-04 - val_loss: 0.0098
Epoch 30/100

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Epoch 31/100

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213235/213235 [==============================] - 10s - loss: 4.3683e-04 - val_loss: 0.0098
Epoch 32/100

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Epoch 33/100

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Epoch 34/100

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Epoch 35/100

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Epoch 36/100

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Epoch 37/100

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213235/213235 [==============================] - 10s - loss: 4.3660e-04 - val_loss: 0.0099
Epoch 38/100

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Epoch 39/100

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Epoch 40/100

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213235/213235 [==============================] - 10s - loss: 4.3654e-04 - val_loss: 0.0097
Epoch 41/100

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Epoch 42/100

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213235/213235 [==============================] - 10s - loss: 4.3645e-04 - val_loss: 0.0097
Epoch 43/100

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213235/213235 [==============================] - 10s - loss: 4.3639e-04 - val_loss: 0.0098
Epoch 44/100

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Epoch 45/100

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213235/213235 [==============================] - 10s - loss: 4.3643e-04 - val_loss: 0.0098
Epoch 46/100

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Epoch 47/100

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Epoch 48/100

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213235/213235 [==============================] - 10s - loss: 4.3633e-04 - val_loss: 0.0098
Epoch 49/100

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Epoch 50/100

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Epoch 51/100

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Epoch 52/100

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Epoch 53/100

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Epoch 54/100

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213235/213235 [==============================] - 10s - loss: 4.3626e-04 - val_loss: 0.0099
Epoch 55/100

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Epoch 56/100

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Epoch 57/100

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Epoch 58/100

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Epoch 59/100

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213235/213235 [==============================] - 10s - loss: 4.3618e-04 - val_loss: 0.0098
Epoch 60/100

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Epoch 61/100

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Epoch 62/100

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Epoch 63/100

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Epoch 64/100

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Epoch 65/100

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Epoch 66/100

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Epoch 67/100

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Epoch 68/100

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213235/213235 [==============================] - 10s - loss: 4.3614e-04 - val_loss: 0.0098
Epoch 69/100

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Epoch 70/100

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Epoch 71/100

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Epoch 72/100

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Epoch 73/100

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213235/213235 [==============================] - 10s - loss: 4.3611e-04 - val_loss: 0.0097
Epoch 74/100

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213235/213235 [==============================] - 10s - loss: 4.3614e-04 - val_loss: 0.0097
Epoch 75/100

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Epoch 76/100

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213235/213235 [==============================] - 10s - loss: 4.3613e-04 - val_loss: 0.0098
Epoch 77/100

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213235/213235 [==============================] - 10s - loss: 4.3603e-04 - val_loss: 0.0098
Epoch 78/100

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Epoch 79/100

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213235/213235 [==============================] - 10s - loss: 4.3611e-04 - val_loss: 0.0098
Epoch 80/100

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Epoch 81/100

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Epoch 82/100

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Epoch 83/100

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Epoch 84/100

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Epoch 85/100

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Epoch 86/100

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Epoch 87/100

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213235/213235 [==============================] - 10s - loss: 4.3611e-04 - val_loss: 0.0097
Epoch 88/100

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Epoch 89/100

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Epoch 90/100

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Epoch 91/100

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Epoch 92/100

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Epoch 93/100

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213235/213235 [==============================] - 10s - loss: 4.3608e-04 - val_loss: 0.0098
Epoch 94/100

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Epoch 95/100

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Epoch 96/100

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Epoch 97/100

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Epoch 98/100

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Epoch 99/100

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213235/213235 [==============================] - 10s - loss: 4.3609e-04 - val_loss: 0.0098
Epoch 100/100

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213235/213235 [==============================] - 10s - loss: 4.3608e-04 - val_loss: 0.0098

tensorboard(log_dir = "logs/run_a")

Started TensorBoard at http://127.0.0.1:4439 

#calculating the mean squared error
pred.train<-predict(model,x_train)
pred.test = predict(model,x_test)
mse_train<- apply((x_train-pred.train)^2,1,sum)
mse_test<-apply((x_test-pred.test)^2,1,sum)